The research of the Neuroinformatics Group is focused on understanding how information processing and cognitive phenomena can arise from the collective self-organization of elements interacting across many spatial and temporal scales.

Understanding the principles of neuronal information processing requires the combination of theoretical, computational and experimental approaches. Therefore research is multidisciplinary and is composed of two tracks. The first track develops and uses analytical and computational models to identify and understand principles. The second track is data-driven and aims to characterize neuronal activity and collective behavior based upon the experimental work of this group and also upon that of international collaborators. The long term goal of the research group is to identify principles that shape neuronal activity and are used to process information in a multi-scale system like the brain.

Main research areas of the Neuroinformatics Group:

• synchronization of neuronal activity in delay-coupled systems
• information processing in self-organized complex systems in different dynamical states, i.e. self-organized criticality
• the use of time series analysis to understand how information flow can take place between neural activity occurring at different spatial and temporal scales